• DocumentCode
    2715386
  • Title

    A Novel Multi-target Detecting and Tracing Method for Robot Vision System

  • Author

    Benjie, Wei ; Peng, Li

  • Author_Institution
    Center for Space Sci. & Appl. Res., Electron. Lab., Beijing, China
  • fYear
    2012
  • fDate
    11-13 Aug. 2012
  • Firstpage
    182
  • Lastpage
    186
  • Abstract
    In this paper we study on multi-target detecting and tracing system for intelligent robot and give a good method to detect and track the targets such as pedestrians, which needs to extract the moving targets from the background in image sequence, and track them by probability statistics and controlling theory. In the first stage, we segment the foreground and background regions through GMM (Gaussian Mixture Model) [1] algorithm. Based on the results of the first stage, we build the tracing system with Kalman filter and Mean-Shift in order to capture the moving targets. At last, the experimental results show that our method is correct and robust, it lays a solid foundation for further study on target recognition.
  • Keywords
    Gaussian processes; Kalman filters; image segmentation; image sequences; intelligent robots; object detection; object tracking; probability; robot vision; GMM algorithm; Gaussian mixture mode algorithm; Kalman filter; background regions; controlling theory; foreground regions; image segmentation; image sequence; intelligent robot; mean-shift algorithm; moving target extraction; novel multitarget detecting method; novel multitarget tracing method; probability statistics; robot vision system; target recognition; Computational modeling; Gaussian distribution; Kalman filters; Kernel; Prediction algorithms; Target tracking; Vectors; GMM; Kalman filter; Mean-Shift; detecting; multi-target; tracing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science & Service System (CSSS), 2012 International Conference on
  • Conference_Location
    Nanjing
  • Print_ISBN
    978-1-4673-0721-5
  • Type

    conf

  • DOI
    10.1109/CSSS.2012.53
  • Filename
    6394292